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  1. Book: Vaccine informatics

    He, Yongqun Oliver

    (Journal of biomedicine and biotechnology ; 2010, Spec. iss.)

    2010  

    Author's details guest ed.: Yongqun Oliver He
    Series title Journal of biomedicine and biotechnology ; 2010, Spec. iss.
    Journal of biomedicine & biotechnology
    Collection Journal of biomedicine & biotechnology
    Language English
    Size Getr. Zählung : Ill., graph. Darst.
    Publisher Hindawi
    Publishing place S.l.
    Publishing country United States
    Document type Book
    HBZ-ID HT016938222
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    Zs A 5540 -2010,Spec.iss.- not available for loan Zeitschriften-Lesesaal

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  2. Article ; Online: Development and Applications of Interoperable Biomedical Ontologies for Integrative Data and Knowledge Representation and Multiscale Modeling in Systems Medicine.

    He, Yongqun

    Methods in molecular biology (Clifton, N.J.)

    2022  Volume 2486, Page(s) 233–244

    Abstract: The data FAIR Guiding Principles state that all data should be Findable, Accessible, Interoperable, and Reusable. Ontology is critical to data integration, sharing, and analysis. Given thousands of ontologies have been developed in the era of artificial ... ...

    Abstract The data FAIR Guiding Principles state that all data should be Findable, Accessible, Interoperable, and Reusable. Ontology is critical to data integration, sharing, and analysis. Given thousands of ontologies have been developed in the era of artificial intelligence, it is critical to have interoperable ontologies to support standardized data and knowledge presentation and reasoning. For interoperable ontology development, the eXtensible ontology development (XOD) strategy offers four principles including ontology term reuse, semantic alignment, ontology design pattern usage, and community extensibility. Many software programs are available to help implement these principles. As a demonstration, the XOD strategy is applied to developing the interoperable Coronavirus Infectious Disease Ontology (CIDO). Various applications of interoperable ontologies, such as COVID-19 and kidney precision medicine research, are also introduced in this chapter.
    MeSH term(s) Artificial Intelligence ; Biological Ontologies ; COVID-19 ; Humans ; Software ; Systems Analysis
    Language English
    Publishing date 2022-04-18
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-2265-0_12
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Editorial: Host cellular responses to viruses.

    He, Yongqun / Li, Yongqing / Cao, Yongchang / Liu, Jue

    Frontiers in microbiology

    2023  Volume 14, Page(s) 1110197

    Language English
    Publishing date 2023-01-10
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2023.1110197
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Microbial Modulation of Host Apoptosis and Pyroptosis

    Amer, Amal O. / He, Yongqun

    2014  

    Abstract: Infectious disease is the result of an interactive relationship between a microbial pathogen and its host. In this interaction both the host and the pathogen attempt to manipulate each other using a complex network to maximize their respective survival ... ...

    Abstract Infectious disease is the result of an interactive relationship between a microbial pathogen and its host. In this interaction both the host and the pathogen attempt to manipulate each other using a complex network to maximize their respective survival probabilities. Programmed host cell death is a direct outcome of host-pathogen interaction and may benefit host or pathogen depending on microbial pathogenesis. Apoptosis and pyroptosis are two common programmed cell death types induced by various microbial infections. Apoptosis is non-inflammatory programmed cell death and can be triggered through intrinsic or extrinsic pathways and with or without the contribution of mitochondria. Pyroptosis is an inflammatory cell death and is typically triggered by caspase-1 after its activation by various inflammasomes. However, some non-canonical caspase-1-independent proinflammatory cell death phenomena have been reported. Microbial pathogens are able to modulate host apoptosis and pyroptosis through different triggers and pathways. The promotion and inhibition of host apoptosis and pyroptosis vary and depend on the microbe types, virulence, and phenotypes. For example, virulent pathogens and attenuated vaccine strains may use different pathways to modulate host cell death. Specific microbial genes may be responsible for the modulation of host cell death. Different host cells, including macrophages, dendritic cells, and T cells, can undergo apoptosis and pyroptosis after microbial infections. The pathways of host apoptosis and pyroptosis induced by different microbes may also differ. Different methods can be used to study the interaction between microbes and host cell death system. The articles included in this E-book report the cutting edge findings in the areas of microbial modulation of host apoptosis, pyroptosis and inflammasome
    Keywords Infectious and parasitic diseases ; Science (General)
    Size 1 electronic resource (109 p.)
    Publisher Frontiers Media SA
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020090203
    ISBN 9782889192809 ; 2889192806
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  5. Article ; Online: Big knowledge visualization of the COVID-19 CIDO ontology evolution

    Ling Zheng / Yehoshua Perl / Yongqun He

    BMC Medical Informatics and Decision Making, Vol 23, Iss S1, Pp 1-

    2023  Volume 19

    Abstract: Abstract Background The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) ... ...

    Abstract Abstract Background The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the largest one. Furthermore, it keeps growing very frequently. Researchers using CIDO as a reference ontology, need a quick update about the content added in a recent release to know how relevant the new concepts are to their research needs. Although CIDO is only a medium size ontology, it is still a large knowledge base posing a challenge for a user interested in obtaining the “big picture” of content changes between releases. Both a theoretical framework and a proper visualization are required to provide such a “big picture”. Methods The child-of-based layout of the weighted aggregate partial-area taxonomy summarization network (WAT) provides a “big picture” convenient visualization of the content of an ontology. In this paper we address the “big picture” of content changes between two releases of an ontology. We introduce a new DIFF framework named Diff Weighted Aggregate Taxonomy (DWAT) to display the differences between the WATs of two releases of an ontology. We use a layered approach which consists first of a DWAT of major subjects in CIDO, and then drill down a major subject of interest in the top-level DWAT to obtain a DWAT of secondary subjects and even further refined layers. Results A visualization of the Diff Weighted Aggregate Taxonomy is demonstrated on the CIDO ontology. The evolution of CIDO between 2020 and 2022 is demonstrated in two perspectives. Drilling down for a DWAT of secondary subject networks is also demonstrated. We illustrate how the DWAT of CIDO provides insight into its evolution. Conclusions The new Diff Weighted Aggregate Taxonomy enables a layered approach to view the “big picture” of the changes in the content between two releases of an ontology.
    Keywords Big knowledge visualization ; COVID-19 ontology ; Coronavirus ontology ; CIDO ontology ; Aggregate partial-area taxonomy ; Summarization network ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 401
    Language English
    Publishing date 2023-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Editorial: Omics approach to study the biology and virulence of microorganisms causing zoonotic diseases.

    Salmon-Divon, Mali / He, Yongqun Oliver / Kornspan, David / Wen, Zezhang Tom

    Frontiers in microbiology

    2022  Volume 13, Page(s) 988983

    Language English
    Publishing date 2022-08-01
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2022.988983
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Big knowledge visualization of the COVID-19 CIDO ontology evolution.

    Zheng, Ling / Perl, Yehoshua / He, Yongqun

    BMC medical informatics and decision making

    2023  Volume 23, Issue Suppl 1, Page(s) 88

    Abstract: Background: The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the ... ...

    Abstract Background: The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the largest one. Furthermore, it keeps growing very frequently. Researchers using CIDO as a reference ontology, need a quick update about the content added in a recent release to know how relevant the new concepts are to their research needs. Although CIDO is only a medium size ontology, it is still a large knowledge base posing a challenge for a user interested in obtaining the "big picture" of content changes between releases. Both a theoretical framework and a proper visualization are required to provide such a "big picture".
    Methods: The child-of-based layout of the weighted aggregate partial-area taxonomy summarization network (WAT) provides a "big picture" convenient visualization of the content of an ontology. In this paper we address the "big picture" of content changes between two releases of an ontology. We introduce a new DIFF framework named Diff Weighted Aggregate Taxonomy (DWAT) to display the differences between the WATs of two releases of an ontology. We use a layered approach which consists first of a DWAT of major subjects in CIDO, and then drill down a major subject of interest in the top-level DWAT to obtain a DWAT of secondary subjects and even further refined layers.
    Results: A visualization of the Diff Weighted Aggregate Taxonomy is demonstrated on the CIDO ontology. The evolution of CIDO between 2020 and 2022 is demonstrated in two perspectives. Drilling down for a DWAT of secondary subject networks is also demonstrated. We illustrate how the DWAT of CIDO provides insight into its evolution.
    Conclusions: The new Diff Weighted Aggregate Taxonomy enables a layered approach to view the "big picture" of the changes in the content between two releases of an ontology.
    MeSH term(s) Humans ; COVID-19 ; Pandemics ; Knowledge ; Knowledge Bases
    Language English
    Publishing date 2023-05-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-023-02184-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation.

    Zhang, Yuhan / Huffman, Anthony / Johnson, Justin / He, Yongqun

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Reverse vaccinology (RV) provides a systematic approach to identifying potential vaccine candidates based on protein sequences. The integration of machine learning (ML) into this process has greatly enhanced our ability to predict viable vaccine ... ...

    Abstract Reverse vaccinology (RV) provides a systematic approach to identifying potential vaccine candidates based on protein sequences. The integration of machine learning (ML) into this process has greatly enhanced our ability to predict viable vaccine candidates from these sequences. We have previously developed a Vaxign-ML program based on the eXtreme Gradient Boosting (XGBoost). In this study, we further extend our work to develop a Vaxign-DL program based on deep learning techniques. Deep neural networks assemble non-linear models and learn multilevel abstraction of data using hierarchically structured layers, offering a data-driven approach in computational design models. Vaxign-DL uses a three-layer fully connected neural network model. Using the same bacterial vaccine candidate training data as used in Vaxign-ML development, Vaxign-DL was able to achieve an Area Under the Receiver Operating Characteristic of 0.94, specificity of 0.99, sensitivity of 0.74, and accuracy of 0.96. Using the Leave-One-Pathogen-Out Validation (LOPOV) method, Vaxign-DL was able to predict vaccine candidates for 10 pathogens. Our benchmark study shows that Vaxign-DL achieved comparable results with Vaxign-ML in most cases, and our method outperforms Vaxi-DL in the accurate prediction of bacterial protective antigens.
    Language English
    Publishing date 2023-12-01
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.11.29.569096
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Ontology-based Vaccine and Drug Adverse Event Representation and Theory-guided Systematic Causal Network Analysis toward Integrative Pharmacovigilance Research.

    He, Yongqun

    Current pharmacology reports

    2016  Volume 2, Issue 3, Page(s) 113–128

    Abstract: Compared with controlled terminologies ( ...

    Abstract Compared with controlled terminologies (
    Language English
    Publishing date 2016-03-11
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2198-641X
    ISSN 2198-641X
    DOI 10.1007/s40495-016-0055-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Ontological representation, modeling, and analysis of parasite vaccines.

    Huffman, Anthony / Zhang, Xumeng / Lanka, Meghana / Zheng, Jie / Masci, Anna Maria / He, Yongqun

    Journal of biomedical semantics

    2024  Volume 15, Issue 1, Page(s) 4

    Abstract: Background: Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines ...

    Abstract Background: Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase. VIOLIN utilizes the Vaccine Ontology (VO) to standardize the modeling of vaccine data. VO did not model complex life cycles as seen in parasites. With the inclusion of successful parasite vaccines, an update in parasite vaccine modeling was needed.
    Results: VIOLIN was expanded to include 258 parasite vaccines against 23 protozoan species, and 607 new parasite vaccine-related terms were added to VO since 2022. The updated VO design for parasite vaccines accounts for parasite life stages and for transmission-blocking vaccines. A total of 356 terms from the Ontology of Parasite Lifecycle (OPL) were imported to VO to help represent the effect of different parasite life stages. A new VO class term, 'transmission-blocking vaccine,' was added to represent vaccines able to block infectious transmission, and one new VO object property, 'blocks transmission of pathogen via vaccine,' was added to link vaccine and pathogen in which the vaccine blocks the transmission of the pathogen. Additionally, our Gene Set Enrichment Analysis (GSEA) of 140 parasite antigens used in the parasitic vaccines identified enriched features. For example, significant patterns, such as signal, plasma membrane, and entry into host, were found in the antigens of the vaccines against two parasite species: Plasmodium falciparum and Toxoplasma gondii. The analysis found 18 out of the 140 parasite antigens involved with the malaria disease process. Moreover, a majority (15 out of 54) of P. falciparum parasite antigens are localized in the cell membrane. T. gondii antigens, in contrast, have a majority (19/24) of their proteins related to signaling pathways. The antigen-enriched patterns align with the life cycle stage patterns identified in our ontological parasite vaccine modeling.
    Conclusions: The updated VO modeling and GSEA analysis capture the influence of the complex parasite life cycles and their associated antigens on vaccine development.
    MeSH term(s) Biological Ontologies ; Animals ; Parasites/immunology ; Protozoan Vaccines/immunology ; Humans ; Vaccines/immunology ; Models, Biological
    Chemical Substances Protozoan Vaccines ; Vaccines
    Language English
    Publishing date 2024-04-25
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2548651-2
    ISSN 2041-1480 ; 2041-1480
    ISSN (online) 2041-1480
    ISSN 2041-1480
    DOI 10.1186/s13326-024-00307-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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